bug fix

Author: AtsushiSakai

PathTracking/pure_pursuit/pure_pursuit.py

@@ -14,6 +14,8 @@
 
 Kp = 1.0  # speed propotional gain
 Lf = 1.0  # look-ahead distance
+#  animation = True
+animation = False
 
 
 def PIDControl(target, current):
@@ -29,16 +31,23 @@ def pure_pursuit_control(state, cx, cy, pind):
     if pind >= ind:
         ind = pind
 
-    tx = cx[ind]
-    ty = cy[ind]
+    #  print(pind, ind)
+    if ind < len(cx):
+        tx = cx[ind]
+        ty = cy[ind]
+    else:
+        tx = cx[-1]
+        ty = cy[-1]
+        ind = len(cx) - 1
 
     alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
 
     if state.v < 0:  # back
-        if alpha > 0:
-            alpha = math.pi - alpha
-        else:
-            alpha = math.pi + alpha
+        alpha = math.pi - alpha
+        #  if alpha > 0:
+        #  alpha = math.pi - alpha
+        #  else:
+        #  alpha = math.pi + alpha
 
     delta = math.atan2(2.0 * unicycle_model.L * math.sin(alpha) / Lf, 1.0)
 
@@ -64,48 +73,56 @@ def calc_target_index(state, cx, cy):
     return ind
 
 
-def closed_loop_prediction(cx, cy, cyaw, speed_profile):
+def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
 
-    T = 100.0  # max simulation time
+    T = 500.0  # max simulation time
+    goal_dis = 0.3
+    stop_speed = 0.05
 
     state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
 
-    lastIndex = len(cx) - 1
+    #  lastIndex = len(cx) - 1
     time = 0.0
     x = [state.x]
     y = [state.y]
     yaw = [state.yaw]
     v = [state.v]
     t = [0.0]
     target_ind = calc_target_index(state, cx, cy)
-    #  print(target_ind)
 
-    while T >= time and lastIndex > target_ind:
+    while T >= time:
         di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
         ai = PIDControl(speed_profile[target_ind], state.v)
         state = unicycle_model.update(state, ai, di)
 
-        if abs(state.v) <= 0.05:
+        if abs(state.v) <= stop_speed:
             target_ind += 1
 
         time = time + unicycle_model.dt
 
+        # check goal
+        dx = state.x - goal[0]
+        dy = state.y - goal[1]
+        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+            print("Goal")
+            break
+
         x.append(state.x)
         y.append(state.y)
         yaw.append(state.yaw)
         v.append(state.v)
         t.append(time)
 
-        plt.cla()
-        plt.plot(cx, cy, "-r", label="course")
-        plt.plot(x, y, "ob", label="trajectory")
-        plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
-        plt.axis("equal")
-        plt.grid(True)
-        plt.title("speed:" + str(round(state.v, 2)) +
-                  "tind:" + str(target_ind))
-        plt.pause(0.0001)
-        #  input()
+        if target_ind % 20 == 0 and animation:
+            plt.cla()
+            plt.plot(cx, cy, "-r", label="course")
+            plt.plot(x, y, "ob", label="trajectory")
+            plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
+            plt.axis("equal")
+            plt.grid(True)
+            plt.title("speed:" + str(round(state.v, 2)) +
+                      "tind:" + str(target_ind))
+            plt.pause(0.0001)
 
     return t, x, y, yaw, v
 
@@ -185,45 +202,31 @@ def calc_speed_profile(cx, cy, cyaw, target_speed, a):
                 speed_profile[-i - 1] = tspeed
 
     #  flg, ax = plt.subplots(1)
-    plt.plot(speed_profile, "-r")
-    #  plt.plot(cx, cy, "-r")
-    plt.show()
+    #  plt.plot(speed_profile, "-r")
+    #  plt.show()
 
     return speed_profile
 
 
-def main():
-    import pandas as pd
-    data = pd.read_csv("rrt_course.csv")
-    cx = np.array(data["x"])
-    cy = np.array(data["y"])
-    cyaw = np.array(data["yaw"])
-
-    target_speed = 10.0 / 3.6
-    a = 0.1
+def extend_path(cx, cy, cyaw):
 
-    speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed, a)
+    dl = 0.1
+    dl_list = [dl] * (int(Lf / dl) + 0)
 
-    t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, speed_profile)
+    move_direction = math.atan2(cy[-1] - cy[-2], cx[-1] - cx[-2])
+    is_back = abs(move_direction - cyaw[-1]) >= math.pi / 2.0
 
-    flg, ax = plt.subplots(1)
-    plt.plot(cx, cy, ".r", label="course")
-    plt.plot(x, y, "-b", label="trajectory")
-    plt.legend()
-    plt.xlabel("x[m]")
-    plt.ylabel("y[m]")
-    plt.axis("equal")
-    plt.grid(True)
+    for idl in dl_list:
+        if is_back:
+            idl *= -1
+        cx = np.append(cx, cx[-1] + idl * math.cos(cyaw[-1]))
+        cy = np.append(cy, cy[-1] + idl * math.sin(cyaw[-1]))
+        cyaw = np.append(cyaw, cyaw[-1])
 
-    flg, ax = plt.subplots(1)
-    plt.plot(t, [iv * 3.6 for iv in v], "-r")
-    plt.xlabel("Time[s]")
-    plt.ylabel("Speed[km/h]")
-    plt.grid(True)
-    plt.show()
+    return cx, cy, cyaw
 
 
-def main2():
+def main():
     #  target course
     import numpy as np
     cx = np.arange(0, 50, 0.1)
@@ -233,8 +236,8 @@ def main2():
 
     T = 15.0  # max simulation time
 
-    #  state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
-    state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
+    state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
+    #  state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
     #  state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
     #  state = unicycle_model.State(
     #  x=3.0, y=5.0, yaw=math.radians(-40.0), v=-10.0 / 3.6)
@@ -289,6 +292,43 @@ def main2():
     plt.show()
 
 
+def main2():
+    import pandas as pd
+    data = pd.read_csv("rrt_course.csv")
+    cx = np.array(data["x"])
+    cy = np.array(data["y"])
+    cyaw = np.array(data["yaw"])
+
+    target_speed = 10.0 / 3.6
+    a = 0.1
+
+    goal = [cx[-1], cy[-1]]
+
+    cx, cy, cyaw = extend_path(cx, cy, cyaw)
+
+    speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed, a)
+
+    t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, speed_profile, goal)
+
+    flg, ax = plt.subplots(1)
+    plt.plot(cx, cy, ".r", label="course")
+    plt.plot(x, y, "-b", label="trajectory")
+    plt.plot(goal[0], goal[1], "xg", label="goal")
+    plt.legend()
+    plt.xlabel("x[m]")
+    plt.ylabel("y[m]")
+    plt.axis("equal")
+    plt.grid(True)
+
+    flg, ax = plt.subplots(1)
+    plt.plot(t, [iv * 3.6 for iv in v], "-r")
+    plt.xlabel("Time[s]")
+    plt.ylabel("Speed[km/h]")
+    plt.grid(True)
+    plt.show()
+
+
 if __name__ == '__main__':
     print("Pure pursuit path tracking simulation start")
-    main()
+    #  main()
+    main2()